1221 lines
45 KiB
Plaintext
1221 lines
45 KiB
Plaintext
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"source": [
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"# 网络中的网络（NiN）\n",
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":label:`sec_nin`\n",
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"\n",
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"LeNet、AlexNet和VGG都有一个共同的设计模式：通过一系列的卷积层与汇聚层来提取空间结构特征；然后通过全连接层对特征的表征进行处理。\n",
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"AlexNet和VGG对LeNet的改进主要在于如何扩大和加深这两个模块。\n",
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"或者，可以想象在这个过程的早期使用全连接层。然而，如果使用了全连接层，可能会完全放弃表征的空间结构。\n",
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"*网络中的网络*（*NiN*）提供了一个非常简单的解决方案：在每个像素的通道上分别使用多层感知机 :cite:`Lin.Chen.Yan.2013`\n",
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"\n",
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"## (**NiN块**)\n",
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"\n",
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"回想一下，卷积层的输入和输出由四维张量组成，张量的每个轴分别对应样本、通道、高度和宽度。\n",
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"另外，全连接层的输入和输出通常是分别对应于样本和特征的二维张量。\n",
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"NiN的想法是在每个像素位置（针对每个高度和宽度）应用一个全连接层。\n",
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"如果我们将权重连接到每个空间位置，我们可以将其视为$1\\times 1$卷积层（如 :numref:`sec_channels`中所述），或作为在每个像素位置上独立作用的全连接层。\n",
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"从另一个角度看，即将空间维度中的每个像素视为单个样本，将通道维度视为不同特征（feature）。\n",
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"\n",
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" :numref:`fig_nin`说明了VGG和NiN及它们的块之间主要架构差异。\n",
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"NiN块以一个普通卷积层开始，后面是两个$1 \\times 1$的卷积层。这两个$1 \\times 1$卷积层充当带有ReLU激活函数的逐像素全连接层。\n",
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"第一层的卷积窗口形状通常由用户设置。\n",
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"随后的卷积窗口形状固定为$1 \\times 1$。\n",
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"\n",
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"\n",
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":width:`600px`\n",
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":label:`fig_nin`\n"
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]
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"id": "9b116832",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:19:49.388461Z",
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"iopub.status.busy": "2023-08-18T07:19:49.387694Z",
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from d2l import torch as d2l\n",
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"\n",
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"\n",
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"def nin_block(in_channels, out_channels, kernel_size, strides, padding):\n",
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" return nn.Sequential(\n",
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" nn.Conv2d(in_channels, out_channels, kernel_size, strides, padding),\n",
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" nn.ReLU(),\n",
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" nn.Conv2d(out_channels, out_channels, kernel_size=1), nn.ReLU(),\n",
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" nn.Conv2d(out_channels, out_channels, kernel_size=1), nn.ReLU())"
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]
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"metadata": {
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"origin_pos": 5
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},
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"source": [
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"## [**NiN模型**]\n",
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"\n",
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"最初的NiN网络是在AlexNet后不久提出的，显然从中得到了一些启示。\n",
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"NiN使用窗口形状为$11\\times 11$、$5\\times 5$和$3\\times 3$的卷积层，输出通道数量与AlexNet中的相同。\n",
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"每个NiN块后有一个最大汇聚层，汇聚窗口形状为$3\\times 3$，步幅为2。\n",
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"\n",
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"NiN和AlexNet之间的一个显著区别是NiN完全取消了全连接层。\n",
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"相反，NiN使用一个NiN块，其输出通道数等于标签类别的数量。最后放一个*全局平均汇聚层*（global average pooling layer），生成一个对数几率\t（logits）。NiN设计的一个优点是，它显著减少了模型所需参数的数量。然而，在实践中，这种设计有时会增加训练模型的时间。\n"
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]
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"id": "8ba4ca30",
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"execution": {
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"iopub.execute_input": "2023-08-18T07:19:52.246912Z",
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"pytorch"
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"source": [
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"net = nn.Sequential(\n",
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" nin_block(1, 96, kernel_size=11, strides=4, padding=0),\n",
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" nn.MaxPool2d(3, stride=2),\n",
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" nin_block(96, 256, kernel_size=5, strides=1, padding=2),\n",
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" nn.MaxPool2d(3, stride=2),\n",
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" nin_block(256, 384, kernel_size=3, strides=1, padding=1),\n",
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" nn.MaxPool2d(3, stride=2),\n",
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" nn.Dropout(0.5),\n",
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" # 标签类别数是10\n",
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" nin_block(384, 10, kernel_size=3, strides=1, padding=1),\n",
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" nn.AdaptiveAvgPool2d((1, 1)),\n",
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" # 将四维的输出转成二维的输出，其形状为(批量大小,10)\n",
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" nn.Flatten())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76c9e9ac",
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"metadata": {
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"origin_pos": 10
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},
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"source": [
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"我们创建一个数据样本来[**查看每个块的输出形状**]。\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "2917eb46",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:19:52.298946Z",
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"iopub.status.busy": "2023-08-18T07:19:52.298371Z",
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"iopub.status.idle": "2023-08-18T07:19:52.325792Z",
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"shell.execute_reply": "2023-08-18T07:19:52.324591Z"
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},
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"origin_pos": 12,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sequential output shape:\t torch.Size([1, 96, 54, 54])\n",
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"MaxPool2d output shape:\t torch.Size([1, 96, 26, 26])\n",
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"Sequential output shape:\t torch.Size([1, 256, 26, 26])\n",
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"MaxPool2d output shape:\t torch.Size([1, 256, 12, 12])\n",
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"Sequential output shape:\t torch.Size([1, 384, 12, 12])\n",
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"MaxPool2d output shape:\t torch.Size([1, 384, 5, 5])\n",
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"Dropout output shape:\t torch.Size([1, 384, 5, 5])\n",
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"Sequential output shape:\t torch.Size([1, 10, 5, 5])\n",
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"AdaptiveAvgPool2d output shape:\t torch.Size([1, 10, 1, 1])\n",
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"Flatten output shape:\t torch.Size([1, 10])\n"
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]
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}
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],
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"source": [
|
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"X = torch.rand(size=(1, 1, 224, 224))\n",
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"for layer in net:\n",
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" X = layer(X)\n",
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" print(layer.__class__.__name__,'output shape:\\t', X.shape)"
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||
]
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||
},
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{
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"cell_type": "markdown",
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"id": "95a4c245",
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"metadata": {
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"origin_pos": 15
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},
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"source": [
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||
"## [**训练模型**]\n",
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"\n",
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"和以前一样，我们使用Fashion-MNIST来训练模型。训练NiN与训练AlexNet、VGG时相似。\n"
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]
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},
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"tab": [
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"pytorch"
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||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
"loss 0.563, train acc 0.786, test acc 0.790\n",
|
||
"3087.6 examples/sec on cuda:0\n"
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"## 小结\n",
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"\n",
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"* NiN使用由一个卷积层和多个$1\\times 1$卷积层组成的块。该块可以在卷积神经网络中使用，以允许更多的每像素非线性。\n",
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"* NiN去除了容易造成过拟合的全连接层，将它们替换为全局平均汇聚层（即在所有位置上进行求和）。该汇聚层通道数量为所需的输出数量（例如，Fashion-MNIST的输出为10）。\n",
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"\n",
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"## 练习\n",
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"\n",
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" 1. 计算量是多少？\n",
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" 1. 预测期间需要多少显存？\n",
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"1. 一次性直接将$384 \\times 5 \\times 5$的表示缩减为$10 \\times 5 \\times 5$的表示，会存在哪些问题？\n"
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]
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"source": [
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"[Discussions](https://discuss.d2l.ai/t/1869)\n"
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]
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